Neutrosophic Sets and Systems
Abstract
Customers’ satisfaction prediction is a vital process for all business organizations to draw new customers and maintain existing customers. One of the most efficient methods to predict customers' satisfaction is classifying customers' feedback. In the real world, customer’s feedback is ambiguous, confusing and inconsistent. This option can be stated in neutrosophic logic as indeterminacy membership, associated with truth and falsity membership. In this study, a classification model based on neutrosophic sets to handle the inconsistency of customer responses is presented. Also, significant factors that impact customers' satisfaction are defined. In order to show the procedures and the application of the proposed method, a case study to determine the customers' satisfaction while using food order application (Talabat) in Egypt is presented. A comparison between the classical classification models and the proposed model based on neutrosophic sets is presented. The experimental results indicate that the proposed classifying model achieved accuracy results around 95.36% to 99.95%, higher than the classical one that achieve around 90.3% to 93.99%. Next, a sensitivity analysis is performed for reliability validation as to determine the most factors that affect customers’ satisfaction.
Recommended Citation
Elsanabary, Walaa and Nouran M. Radwan. "Predicting Customer Satisfaction based on Neutrosophic sets: Applied on mobile food ordering application." Neutrosophic Sets and Systems 80, 1 (2025). https://digitalrepository.unm.edu/nss_journal/vol80/iss1/13
